A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Simulation of high-speed train aerodynamics using RANS and machine learning.
Machine learning improves aircraft performance prediction by analyzing flight data.
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
New friction model for geometric locomotion systems.
DRAG decreases regularization to accelerate semi-discrete OT convergence.
Bayesian framework predicts aerodynamic uncertainty from sparse measurements.
Physics-based simulations are often used to model and understand complex physical systems and processes in domains like fluid dynamics. Such simulations, although used frequently, have many limitations which could arise either due to the inability to accurately model a physical process owing to incomplete knowledge abo…
We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects …
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model…
In a three-dimensional Riemannian manifold M that admits a unit Killing vector field , we regard as a magnetic vector field. A magnetic Hopf surface is a surface obtained by Lie dragging the magnetic curve with . Then we characterize Sasakian structure on M from magnetic Hopf surfaces. That is, we show that i…
This paper identifies a negative profit effect in limit order fills.
Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not str…
Optimizes neural network for aerodynamic predictions with Bayesian Optimization.
We present a new approach for matching regular surfaces in a Riemannian setting. We use a Sobolev type metric on deformation vector fields which form the tangent bundle to the space of surfaces. In this article we compare our approach with the diffeomorphic matching framework. In the latter approach a deformation is pr…
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
We construct the first combinatorial 1-cocycle with values in the -module of isotopy classes of singular long knots in 3-space with a signed planar double point, and which represents a non trivial cohomology class in the topological moduli space of long knots. It can be interpreted as an invaria…
Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based …
ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.
We show how bad and good volatility propagate through forex markets, i.e., we provide evidence for asymmetric volatility connectedness on forex markets. Using high-frequency, intra-day data of the most actively traded currencies over 2007 - 2015 we document the dominating asymmetries in spillovers that are due to bad r…
We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian…
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is t…
In this article we model a financial derivative price as an observable on the market state function. We apply geometric techniques to integrating the Heisenberg Equation of Motion. We illustrate how the non-commutative nature of the model introduces quantum interference effects that can act as either a drag or a boost …
We here present a model of the dynamics of extremism based on opinion dynamics in order to understand the circumstances which favour its emergence and development in large fractions of the general public. Our model is based on the bounded confidence hypothesis and on the evolution of initially anti-conformist agents to…
TODS automates time series outlier detection with customizable pipelines.
We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw…
Design-by-Morphing creates radical airfoil designs without geometric constraints.
New approach uses interpolation models and error bounds for verifiable scientific machine learning.
A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving be…
With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, w…
A new hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.
This study shows ESG ratings reduce equity crash risk during market downturns.
Local GP approach improves simulation efficiency for large datasets.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…
The paradox of the energy transition is that the low marginal costs of new renewable energy sources (RES) drag electricity prices down and discourage investments in flexible productions that are needed to compensate for the lack of dispatchability of the new RES. The energy transition thus discourages the investments t…
Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.…
Formula connects linking coefficients to Kontsevich integral coefficients.
Computed distortion coefficients for the α-Grushin plane.
Machine learning predicts Kronecker coefficients with high accuracy.
Abstract: Determines thermoelastic coefficients from boundary data.
New filling functions for groups with coefficients show different asymptotic behavior.
Abstract: Determines Lamé coefficients from boundary measurements.
Defines and proves properties of weighted renormalized volume coefficients.
Improved 3D LiDAR data classification using product coefficients.
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
We study the structure of the stable coefficients of the Jones polynomial of an alternating link. We start by identifying the first four stable coefficients with polynomial invariants of a (reduced) Tait graph of the link projection. This leads us to introduce a free polynomial algebra of invariants of graphs whose ele…
Paper calculates third coefficient in Kaehler-Einstein metric expansion.
Develops local elliptic regularity for geometrically-natural operators with low regularity coefficients.
The paper uses Floer homology to study twist coefficients and their behavior after capping off.